Thus the resulted values were off course directly proportional to 
the volumes of buildings, know when adding other factors to 
some particular building types or buildings falling into a certain 
zones, such as those within industrial arcas, or using some 
attributes such as the construction date, further if remote 
sensing will provide from images or Lidar data some parameters 
that can indicate the building material or the greenness of a 
building, as much as if a green roof exits then a further 
refinement can be inserted to the spatial tables and further 
analysis can indicate the relations to the mentioned factors to 
the energy consumption and subsequently the reduction of the 
emitted CO2 caused by power plants. 
2.3 First values and Maps of reduced CO2 
The sample data associated some relationships between reduced 
energy values in KWh and the equivalent carbon dioxide 
associated to it in tones, thus once the reduced energy is 
calculated maps of reduced CO2 can be generated out of which. 
Nonetheless, CO2 values are more sophisticated and can be 
related to many types of factors, such as factors of greenery 
areas, and road surfaces besides many others, but herein those 
tow feature class layers were selected as a start to the 
assessment, the primitive CO2 reduction map is illustrated in 
figure 2 uses five classes of energy reduction in tons of the test 
area, but again linearity to the volume is initially kept and will 
be verified by the other factors in further analysis and 
assessments. 
Energy Map 3 
  
Legend e & NY " 
Build, geodatabasetest m 
CO2 Red to wn 40 
0.014495 - 10.000000 
ps 
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25.000001 - 50.000000 
E88 ~0 000001 - 200000000 
TR 200 000001 - 1000.000000 
3 
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t 
    
  
  
  
Figure 2 CO2 Reduction Map 
These are not actual CO2 values, but values that effect CO2 
produced by the power plant supplying the energy to the area, 
as it's clear from the image above more emission is caused by 
concentrated dwelling areas, the fact which shall be further 
proved when using greenery and transformational feature 
Classes. The histogram generated from this data set as 
illustrated in figure 3 show high repetitions at the lower CO2 
reduction values. 
Col Reduced Factor 
  
   
Figure 3 Data Histogram of CO2 Reduced values 
3. RASTER ANALYSIS AND CO2 VALUES 
The role of GIS in ranking urban sustainability is further 
illustrated by conducting some operations using pixel values. 
3.1 Rasterizing Vector Maps of CO2 Reduction 
Working on the previously shown energy data and map of 
which transferring it to point data, as an intermediate operation 
for rasterizing its values as a step forward to prepare creating 
multi raster layers and perform processes among which. The 
raster map is then generated from vector point map as illustrated 
in figure 4 and data sets are classified and ranked with colours 
for the purpose of showing the frequency of classes using 
colours distributed spatially on the margins of maps. 
  
Col Reduced Factor 
  
  
Figure 4 Rasterized CO2 Reduced values 
Again most of the areas show a lower reduction of CO2 
represented by dark green and followed by lighter green for the 
second class, after which come the other classes occupying only 
a small part of the distributed map. 
To add more values to this map point data is also calculated 
from greenery layers and road surface layers, leaving the other 
open space feature classes to be included in the future analysis 
once more sample energy data will be available, perhaps from 
private villas and flats, also the shape, combination and distance 
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